Unsupervised dense crowd detection by multiscale texture analysis

Abstract : This study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised.
Type de document :
Article dans une revue
Pattern Recognition Letters, Elsevier, 2013, pp.1-27
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Contributeur : Nicolas Courty <>
Soumis le : jeudi 14 novembre 2013 - 09:43:29
Dernière modification le : samedi 23 mars 2019 - 01:28:39
Document(s) archivé(s) le : samedi 15 février 2014 - 04:30:32


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  • HAL Id : hal-00904210, version 1


Fagette Antoine, Nicolas Courty, Daniel Racoceanu, Jean-Yves Dufour. Unsupervised dense crowd detection by multiscale texture analysis. Pattern Recognition Letters, Elsevier, 2013, pp.1-27. 〈hal-00904210〉



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